Refining neural network algorithms for accurate brain tumor classification in MRI imagery

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics

PURPOSE

To detect the Marchiafava Bignami Disease (MBD) using a distinct deep learning technique.

BACKGROUND

Advanced deep learning methods are becoming more crucial in contemporary medical diagnostics, particularly for detecting intricate and uncommon neurological illnesses such as MBD. This rare neurodegenerative disorder, sometimes associated with persistent alcoholism, is characterized by the loss of myelin or tissue death in the corpus callosum. It poses significant diagnostic difficulties owing to its infrequency and the subtle signs it exhibits in its first stages, both clinically and on radiological scans.

METHODS

The novel method of Variational Autoencoders (VAEs) in conjunction with attention mechanisms is used to identify MBD peculiar diseases accurately. VAEs are well-known for their proficiency in unsupervised learning and anomaly detection. They excel at analyzing extensive brain imaging datasets to uncover subtle patterns and abnormalities that traditional diagnostic approaches may overlook, especially those related to specific diseases. The use of attention mechanisms enhances this technique, enabling the model to concentrate on the most crucial elements of the imaging data, similar to the discerning observation of a skilled radiologist. Thus, we utilized the VAE with attention mechanisms in this study to detect MBD. Such a combination enables the prompt identification of MBD and assists in formulating more customized and efficient treatment strategies.

RESULTS

A significant breakthrough in this field is the creation of a VAE equipped with attention mechanisms, which has shown outstanding performance by achieving accuracy rates of over 90% in accurately differentiating MBD from other neurodegenerative disorders.

CONCLUSION

This model, which underwent training using a diverse range of MRI images, has shown a notable level of sensitivity and specificity, significantly minimizing the frequency of false positive results and strengthening the confidence and dependability of these sophisticated automated diagnostic tools.

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm

In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.

UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement

In today's society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user's behavior data to suggest relevant items. The collaborative filtering strategy has been used to provide a user with the top research articles based on their queries and similarities with other users' questions, thus saving time by avoiding time-consuming searches. However, when rating data is abundant but sparse, the usual method of determining user similarity is relatively straightforward. Furthermore, it fails to account for changes in users' interests over time resulting in poor performance. This research proposes a new similarity measure approach that takes both user confidence and time context into account to increase user similarity computation. The experimental results show that the proposed technique works well with sparse data, and improves accuracy by 16.2% compared to existing models, especially during prediction. Furthermore, it enhances the quality of recommendations.

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.

Machine Learning-Based COVID-19 Classification Using E-Adopted CT Scans

In recent years, several machine learning models were successfully deployed in various fields. However, a huge quantity of data is required for training good machine learning. Data are distributivity stored across multiple sources and centralizing those data leads to privacy and security issues. To solve this problem, the proposed federated-based method works by exchanging the parameters of three locally trained machine learning models without compromising privacy. Each machine learning model uses the e-adoption of CT scans for improving their training knowledge. The CT scans are electronically transferred between various medical centers. Proper care is taken to prevent identify loss from the e-adopted data. To normalize the parameters, a novel weighting scheme is also exchanged along with the parameters. Thus, the global model is trained with more heterogeneous samples to increase performance. Based on the experiment, the proposed algorithm has obtained 89% of accuracy, which is 32% more than the existing machine learning models.

Internet of Things (IoT) Enabled Air Quality Monitoring System for Conventional and UAV Application

The purpose of this research is to make a system to read the air quality by detecting the various gases existing in the air by using Arduino UNO and node MCU module for conventional as well as UAV applications. The emergence of the Internet of Things (IoT) has made it easier to read the various gases present in the environment using smartphones from the workplace. The use of an Arduino processor with a Node MCU to construct an air quality monitoring system is discussed in this paper. The Node MCU is used to transmit live data for CO, CO2, and PM2.5 concentrations that are sensed by sensors. This data can be monitored continuously by the user via the mobile phone. The calibration of sensors is highly important while reviewing and grasping the large literature on the subject of IoT-based air quality monitoring. The proposed low-cost live air quality monitoring system uses commercially available gas sensors to detect environmental gases such as CO, CO2 and PM2.5 to monitor air quality in an outdoor area. The proposed system is used to correctly evaluate the experimental outcomes. This proposed prototype model incorporates an open-source cloud facility with Arduino for air quality monitoring, confirming low cost, comfort, and convenience for a customizable air quality monitoring system. As a result, the suggested system can simply be converted to use in a UAV for monitoring air quality in the outdoors at various altitudes, and it can be scaled up in the future.

Tumor Categorization Model (TCM) Using Soft Computing Techniques for Providing Efficient Medical Support in Brain Tumor Treatments

Brain cancer identification and segmentation is a prolonged and difficult task in Medical Image Processing, which is most significant for providing appropriate treatment and increase patient’s life span. With the advancements available in medical fields, soft computing techniques are incorporated to accurate detection and classification of brain tumors. Besides brain cancer detection, it is vital to categorize tumor stage based on their features. For that concern, this paper develops a Tumor Categorization Model (TCM) that includes image processing and soft computing techniques. Here, pre-processing is carried out using modified Gabor filter and segmentation process is performed with OTSU thresholding. Following segmentation, region growing is processed based on the pixel intensities of input MRI brain images. Further, Discrete Wavelet Transform is enforced for extorting image features as well as gray-level co-occurence matrix features are also derived for appropriate classifications. Finally, the input MRI images are classified using Boosting Support Vector Machine (BSVM) with the benchmark dataset called DICOM and BraTS dataset. The experimental results demonstrate accurate brain tumor detection and categorization by the efficient incorporation of image processing and soft computing methodologies, provides efficient clinical support in providing treatments.

Improving Network Security Based on Trust-Aware Routing Protocols Using Long Short-Term Memory-Queuing Segment-Routing Algorithms

Defending all single connection failures for a particular system, segment routing issue, the switch will focus on the problems of selecting a small subset of trust-aware routing to improve the deep learning (DL). In the end, even if there were multiple path failures, these paths may introduce long-term, unnecessary overload in the proposed long short-term memory networks-based queuing routing segmentation (LSTM-QRS) experience of reducing traffic delays and adjusting traffic length by reducing network bandwidth. The critical factor is a novel traffic repair technique used to create a traffic repair path that switches to software-defined network (SDN) using multiple routing and providing additional flexibility in re-routing using long short-term memory networks (LSTM)-based queuing routing segment (LSTM-QRS) algorithms. It reduces the repair path length and recommends replacing the target-based traffic with the connection-based traffic fault detection router to avoid targeted traffic network congestion.

Po uptake in microalgae at different seawater pH: An experimental study simulating ocean acidification

Climate change effects such as ocean acidification (OA) are known to affect the trace metal distribution. This experimental study provides the first data on ²⁰⁹Po uptake rates and ²¹⁰Po concentration in five microalgae species under different pH scenarios. The experiment was conducted in replicates at three pH conditions 8.2, 8.0, and 7.5, representing the current and future climate change scenario as per IPCC RCP8.5. The ²⁰⁹Po uptake in the phytoplankton was highest in Thalassiosira weissflogi, i.e. 83% of the ²⁰⁹Po tracer was taken up at 8.2 pH whereas the lowest uptake was observed in Dunaliella salina equivalent to 20% at 7.5 pH. Similar behavior was observed in ²¹⁰Po concentrations in these microalgae, where ²¹⁰Po ranged between 3.16 ± 0.03 and 11.6 ± 0.04 Bq kg^-1 wet weight (ww), with the highest in the Thalassioria weissflogi at 8.2 pH, and the lowest in Dunaliella salina at 7.5 pH. The difference in ²⁰⁹Po uptake and ²¹⁰Po concentration was statistically significant (p < 0.001) both among species and the pH treatments in the order: Thalassiosira weissflogi > Tetraselmis suecica > Chaetoceros muelleri > Isochrysis galbana > Dunaliella salina and 8.2 > 8.0 > 7.5. A higher concentration of ²⁰⁹Po in seawater was measured at low pH condition in all the experimental tanks. Though the data clearly show the difference in concentration and uptake of polonium at different pH conditions, it is not known if lower pH is affecting the adsorbed or absorbed fraction. A detailed investigation will be required to understand the process as it can have a significant effect on biomagnification and marine food chain transfer under changing climatic scenarios.

Green synthesis of silver nanoparticles using Nardostachys jatamansi and evaluation of its anti-biofilm effect against classical colonizers

In this communication, we present the green synthesis of silver nanoparticles (AgNPs) using medicinally important Nardostachys jatamansi rhizome extract in the presence of sunlight. UV-vis spectroscopy, Fourier Transform Infrared spectroscopy (FTIR), Transmission electron microscope (TEM) and Energy dispersive X-ray analysis (EDX) were employed to characterize the synthesized AgNPs. UV-visible spectroscopic studies confirmed the presence of biosynthesized AgNPs. Transmission Electron Microscopic studies revealed the structure of spherical AgNPs in the diameter range of 10-15 nm. Energy dispersive X-ray analysis and elemental mapping clearly confirmed the presence of silver in AgNPs samples. Interestingly, biomolecules functionalised AgNPs exhibited a remarkable antioxidant, anti-inflammatory, and anti-biofilm activities and hence biosynthesized AgNPs from N. jatamansi can be used as a promising biomaterial for biomedical applications.

Osseous response on linear and cyclic RGD-peptides immobilized on titanium surfaces in vitro and in vivo

Biomimetic surface modifications of titanium (Ti) implants using the Arg-Gly-Asp-sequence (RGD) are promising to accelerate bone healing in cases of medical implants. Therefore, we compared the impact of linear and cyclic RGD (l- and c-RGD) covalently coupled onto Ti surfaces on the osseous response in vitro and in vivo. In vitro, osteoblasts' behavior on different surfaces (unmodified, amino-silanized [APTES], l- and c-RGD) was analysed regarding adhesion (fluorescence microscopy), proliferation (resazurin stain) and differentiation (reverse transcription polymerase chain reaction on alkaline phosphatase and osteocalcin). In vivo, osteosynthesis screws (unmodified n = 8, l-RGD n = 8, c-RGD n = 8) were inserted into the proximal tibiae of 12 rabbits and evaluated for bone growth parameters (bone implant contact [%] and vertical bone apposition [VBA;%]) at 3 and 6 weeks. In vitro, c- as well as l-RGD surfaces stimulated osteoblasts' adherence, proliferation and differentiation in a similar manner, with only subtle evidence of superiority of the c-RGD modifications. In vivo, c-RGD-modifications led to a significantly increased VBA after 3 and 6 weeks. Thus, coating with c-RGD appears to play an important role influencing osteoblasts' behaviour in vitro but especially in vivo. These findings can be applied prospectively to implantable biomaterials with hypothetically improved survival and success rates. © 2017 Wiley Periodicals Inc. J Biomed Mater Res Part A: 106A: 419-427, 2018.

Recyclable cross-linked laccase aggregates coupled to magnetic silica microbeads for elimination of pharmaceuticals from municipal wastewater

In the present work, the use of magnetic mesoporous silica microbeads (MMSMB) as supports was proposed to produce magnetically-separable cross-linked enzyme aggregates (MCLEAs). The effects of cross linking time, addition of bovine serum albumin as protein feeder, pH, glutaraldehyde concentration, and laccase:MMSMB mass ratio on the immobilization yield and enzyme load were investigated. The best conditions allowed the rapid preparation of MCLEAs with high enzyme load, i.e., 1.53 U laccase/mg MCLEAs. The stability of MCLEAs was improved with regard to low pH, presence of chemical denaturants, and real wastewater matrix, compared to free laccase. In addition, the novel biocatalyst exhibited good operational stability, maintaining up to 70 % of its initial activity after 10 successive batch reactions. Finally, MCLEAs demonstrated its catalytic potential to transform acetaminophen and various non-phenolic pharmaceutical active compounds as mefenamic acid, fenofibrate, and indomethacin from biologically treated wastewater effluent, with similar or even higher efficiency than free laccase.

Gemella morbillorum endocarditis in hypertrophic cardiomyopathy: a rare organism causing a large vegetation and abscess in an uncommon setting

Mitral valve endocarditis complicating hypertrophic cardiomyopathy is rare especially in the absence of significant mitral regurgitation. The occurrence of large vegetation and an abscess formation is even rarer. Endocarditis occurs predominantly on the left ventricular aspect of the anterior mitral leaflet. We report a case of a 34-year-old woman with asymptomatic obstructive hypertrophic cardiomyopathy who developed mitral valve endocarditis with large vegetation and subsequently an abscess caused by a rare organism Gemella morbillorum, following dental extraction. The patient underwent antibacterial therapy followed by successful mitral valve replacement for severe mitral regurgitation and her postoperative course has been asymptomatic so far. This case is unique in describing endocarditis by a rare organism as a cause of large vegetation and an abscess on an unusual site on the mitral valve leaflet without predisposition of haemodynamically significant mitral regurgitation and illustrates the potential lifesaving role of timely intervention.

Aneurysmal bone cyst of maxilla existing concurrently with a giant cell granuloma of mandible: a case report of an unusual presentation

Aneurysmal bone cysts (ABCs) are relatively rare but well-described expansile, osteolytic psuedocysts are most commonly seen in the long bones, pelvis, and vertebra. Although a well-described lesion, much confusion exists in its pathogenesis especially of the associations of this lesion with other lesions. Approximately, one-third of patients with ABC present simultaneously with another bone pathology. The aim of this paper is to describe a rare case of simultaneously occurring ABC of the maxilla and mandible with the mandibular lesion occurring predominantly as a giant cell granuloma. The paper discusses the various histopathological variations of ABC as well as different lesions associated with ABC and tries to provide a review of different pathogenesis theories of this interesting lesion.

Preparation and characterization of porous cross linked laccase aggregates for the decolorization of triphenyl methane and reactive dyes

The production of porous cross-linked enzyme aggregates (p-CLEAs) is a simple and effective methodology for laccase immobilization. A three-phase partitioning technique was applied to co-precipitate laccase and starch, followed by cross-linking with glutaraldehyde and removal of starch by α-amylase to create pores in the CLEAs. Scanning electron microscopy revealed a very smooth spherical structure with numerous large pores. The half-life of free laccase at 55°C was calculated to be 1.3h, while p-CLEAs did not lose any activity even after 14 h. p-CLEAs also exhibited improved storage stability, catalytic efficiency and could be recycled 15 times with 60% loss of activity. The catalysts decolorized triphenylmethane and reactive dyes by 60-70% at initial dye concentrations of 2 and 0.5 g L(-1), respectively, without any mediators. These results suggest the potential of CLEA technology in waste-water treatment.

Le Fort I distraction osteogenesis of the edentulous maxilla

Rehabilitation of the edentulous patient with atrophic ridges is a problem especially when compounded with a severe prognathic inter-arch relationship. It is difficult to rehabilitate these patients prosthetically without correction of the malrelation of the jaws. The established surgical techniques for correcting combined sagittal and vertical discrepancies of edentulous jaws are often prolonged and complex with attendant morbidity. This article presents a novel, simple method of correction of severe interarch sagittal discrepancy (more than 15 mm) by performing distraction osteogenesis at Le Fort I level using an internal maxillary distraction device. This method is a simple, predictable and stable option for the correction of a severe, unfavourable intermaxillary relation in edentulous patients.

Effect of quercetin on galactose-induced hyperglycaemic oxidative stress in hepatic and neuronal tissues of Wistar rats

In recent times there has been great demand for natural products that have possible preventive action against diabetes and its secondary complications. Keeping this in mind, this study was undertaken to investigate the influence of the flavonoid, quercetin, on oxidative stress markers and the antioxidant defence system of hepatic and neuronal tissues from galactose-induced hyperglycaemic rats. Weanling male Wistar rats were treated with 30% galactose in AIN 93 diet (group B, n=8) to induce hyperglycaemia. Control rats received normal Stock AIN 93 diet (group A, n=8). The third set of rats received group B diet with quercetin at 400 mg/100 g diet (group C, n=8). Glucose levels and body weights were measured on a weekly basis for four weeks to monitor the hyperglycaemia induced by galactose feeding. Parameters involved in the pathogenesis of galactose-induced hyperglycaemia, which included organosomatic index, protein content, antioxidant enzymes superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), tryptophan fluorescence, content of protein carbonyls, prooxidant malonaldehyde (MDA) and glutathione (GSH) in hepatic and neuronal tissues were determined at the end of the fourth week. The study suggest that quercetin counters the pro-oxidant effects of galactose-induced hyperglycaemic stress, as there was a significant reversal of changes with respect to body weights, organosomatic index of hepatic and neuronal tissues, lipid peroxidation, protein carbonyl content, reduced glutathione and activities of antioxidant enzymes. In addition, treatment with quercetin appears to reduce the osmotic stress induced by hyperglycaemia, as assessed by polyol pathway enzyme aldose reductase. These results imply that inclusion of quercetin in the diet controls, to some extent, galactose-induced hyperglycaemia and its attendant complications.

Single histidine residue in head-group region is sufficient to impart remarkable gene transfection properties to cationic lipids: evidence for histidine-mediated membrane fusion at acidic pH

Presence of endosome-disrupting multiple histidine functionalities in the molecular architecture of cationic polymers, such as polylysine, has previously been demonstrated to significantly enhance their in vitro gene delivery efficiencies. Towards harnessing improved transfection property through covalent grafting of endosome-disrupting single histidine functionality in the molecular structure of cationic lipids, herein, we report on the design, the synthesis and the transfection efficiency of two novel nonglycerol-based histidylated cationic amphiphiles. We found that L-histidine-(N,N-di-n-hexadecylamine)ethylamide (lipid 1) and L-histidine-(N,N-di-n-hexadecylamine,-N-methyl)ethylamide (lipid 2) in combination with cholesterol gave efficient transfections into various cell lines. The transfection efficiency of Chol/lipid 1 lipoplexes into HepG2 cells was two order of magnitude higher than that of FuGENE(TM)6 and DC-Chol lipoplexes, whereas it was similar into A549, 293T7 and HeLa cells. A better efficiency was obtained with Chol/lipid 2 lipoplexes when using the cytosolic luciferase expression vector (pT7Luc) under the control of the bacterial T7 promoter. Membrane fusion activity measurements using fluorescence resonance energy transfer (FRET) technique showed that the histidine head-groups of Chol/lipid 1 liposomes mediated membrane fusion in the pH range 5-7. In addition, the transgene expression results using the T7Luc expression vector convincingly support the endosome-disrupting role of the presently described mono-histidylated cationic transfection lipids and the release of DNA into the cytosol. We conclude that covalent grafting of a single histidine amino acid residue to suitable twin-chain hydrophobic compounds is able to impart remarkable transfection properties on the resulting mono-histidylated cationic amphiphile, presumably via the endosome-disrupting characteristics of the histidine functionalities.

Validated HPLC method for determination of PAT-5A, an insulin sensitizing agent, in rat plasma

A high performance liquid chromatographic method for the determination of PAT-5A (a potent insulin sensitizer) using DRF-2095 (a thiazolidinedione) as internal standard (I.S.) is described. A 1:1 v/v ethylacetate and dichloromethane solvent mixture was used for extraction of PAT-5A from plasma. A Kromasil KR100-5C18-250A, 5 microm, 4.6 x 250 mm SS column was used for the analysis. Mobile phase consisting of sodium dihydrogen phosphate (pH 4.0, 0.05 M) and methanol mixture (25:75, v/v) was used at a flow rate of 1.0 ml/min. The eluate was monitored using a UV detector set at 345 nm. Ratio of peak area of analyte to I.S. was used for quantification of plasma samples. Using this method the absolute recovery of PAT-5A from rat plasma was > 90% and the limit of quantification was 0.05 microg/ml. The intra-day relative standard deviation (RSD) ranged from 2.19 to 4.98% at 1.0 microg/ml, 1.05 to 3.68% at 10.0 microg/ml and 3.14 to 5.08% at 50 microg/ml. The inter-day RSD were 1.6, 2.24 and 1.54% at 1, 10 and 50 microg/ml, respectively. The method was applied to measure the plasma concentrations of PAT-5A in pharmacokinetic and bioavailability studies in male Wistar rats.

Relationship between Oil-Water Interfacial Tension and Oily Soil Removal in Mixed Surfactant Systems

Detergency of oily soil is a complex kinetic process that includes contributions from the wash system physical properties, time and temperature of wash, and the hydrodynamic forces exerted during the wash process. Generally, surfactant mixtures that exhibit a low oil-water interfacial tension are considered to provide superior oily soil detergency. In our investigations, we measured the oil-water interfacial tension for C12EO3/NaLAS and C12EO7/NaLAS blends as a function of temperature (18-80 degreesC) and time (0-60 min). The oil-water interfacial tension was found to decrease as a function of time for all blends containing nonionic surfactants, and this decline was most rapid for blends containing a higher fraction of ethoxylated alcohol with less than 3 mol of ethylene oxide. It is proposed that the diffusion of this hydrophobic fraction into the oil phase leads to the measured decline in the oil-water interfacial tension. Also, due to this limitation, no general correlation of oil-water interfacial tension with oily soil removal was observed. In view of these observations, it appears that the measurement of the oil-water interfacial tension (using a spinning drop tensiometer) for solutions containing hydrophobic surfactants may give misleading results. Copyright 1998 Academic Press.

Prevalence and distribution of the hmw and hia genes and the HMW and Hia adhesins among genetically diverse strains of nontypeable Haemophilus influenzae

Nontypeable Haemophilus influenzae is a common cause of human disease and initiates infection by colonizing the upper respiratory tract. In previous work we identified high-molecular-weight adhesins referred to as HMW1 and HMW2, expressed by nontypeable strain 12, and determined that most strains of nontypeable H. influenzae express one or two antigenically related proteins. More recently, we determined that some strains lack HMW1- and HMW2-like proteins and instead express an adhesin called Hia. In the present study, we determined the prevalence and distribution of the hmw and hia genes in a collection of 59 nontypeable strains previously characterized in terms of genetic relatedness. Based on Southern analysis, 47 strains contained sequences homologous to the hmw1 and hmw2 genes and nine strains contained homologs to hia. No strain harbored both hmw and hia, and three strains harbored neither. Although the hmw and hia genes failed to define distinct genetic divisions, the hmw-deficient strains formed small clusters or lineages within the larger population structure. Additional analysis established that the IS1016 insertion element was uniformly absent from strains containing hmw sequences but was present in two-thirds of the hmw-deficient strains. As IS1016 is associated with the capsule locus (cap) in most encapsulated strains of H. influenzae, we speculate that hmw-deficient nontypeable strains evolved more recently from an encapsulated ancestor.

Lipid molecular shapes and membrane architecture

A simple biomembrane like erythrocyte contains well over hundred lipid species with diverse molecular shapes differing in the number of acyl chains, chain length, unsaturation and head group composition. A delicate balance between these molecular shapes is necessary in order to have a functional membrane. It is well established that the activities of a number of membrane-bound enzymes and other properties such as aggregation, spontaneous vesiculation, pathophysiological properties and lipid-protein interactions of lipids depend on the acyl chain length, unsaturation and head group composition. In fact, the molecular shape of a phospholipid molecule, as modulated by changes in chain length, unsaturation and head group composition, is probably what is affecting the above mentioned properties. The molecular shape of a lipid depends on a dimensionless packing parameter, S, the value of which influences the size and shape of aggregate formed upon hydration. In fact, the additivity of S values of lipid mixtures explains a number of experimental observations. The molecular shape concept, although very simple, explains many membrane phenomena like complementarity of molecular shapes of non-bilayer lipids to form stable bilayers. Membrane permeability is controlled to a large extent by lipid packing which depends upon molecular shapes. In fact, membranes maintain their lamellar structure by delicately balancing the composition of bilayer-forming and non-bilayer-forming lipids indicating that complementarity of molecular shapes is essential to maintain the permeability barrier. Based on this, the complementary molecular shape model of cell membrane is proposed.

Phosphatidyl-Tris rather than N-acylphosphatidylserine is synthesized by Rhodopseudomonas sphaeroides grown in Tris-containing media

We have synthesized 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho(N-oleoyl)serine (N-acyl-PS) and 1,2-dioleoyl-sn-glycero-3-phospho-Tris (phosphatidyl-Tris) and have characterized both phospholipids by their chemical and chromatographic properties, as well as by their IR, 13C NMR, and 1H NMR spectra. Comparison of these data with those reported for a phospholipid isolated from Rhodopseudomonas sphaeroides grown in Tris-supplemented media [Donohue et al. (1982) Biochemistry 21, 2765-2773] indicates that R. sphaeroides synthesizes phosphatidyl-Tris rather than N-acyl-PS.

Lanthanide-induced phosphorus-31 NMR downfield chemical shifts of lysophosphatidylcholines are sensitive to lysophospholipid critical micelle concentration

Lysophosphatidylcholine (lysoPC) monomers or micelles in water give rise to a narrow, isotropic phosphorus-31 NMR signal (40.6 ppm; v1/2 1.7 Hz; 32.2 MHz). Upon addition of praseodymium ions, the phosphorus signals are shifted downfield. However, the downfield shifts for the longer-chain lysophosphatidylcholines, which exist in the aggregated state, are far greater than those for the shorter-chain homologues, which exist as monomers. At a Pr3+/lysoPC molar ratio of 0.5, the signals of C12lysoPC through C18lysoPC were shifted by 12.1 ppm, whereas the signals of C6lysoPC and C8lysoPC were shifted by only 2.26 ppm. This very pronounced difference in lanthanide-induced downfield shifts between micelles and monomers can be utilized to determine with accuracy lysoPC critical micelle concentrations (CMC) from downfield shift-vs.-concentration plots. The CMC values we determined were 57 mM for C8lysoPC, 5.7 mM for C10lysoPC, and 0.6 mM for C12lysoPC. The shift reagent phosphorus-31 nuclear magnetic resonance technique particularly lends itself to the measurement of CMC values in the millimolar and high micromolar range. The method can equally be used for measuring critical micelle concentrations of short-chain phosphatidylcholines.

Lyso platelet activating factor (LysoPAF) and its enantiomer. Total synthesis and carbon-13 NMR spectroscopy

Described is a reaction sequence for the total synthesis of lyso platelet activating factor (lysoPAF; 1-O-alkyl-sn-glycero-3-phosphocholine) and its enantiomer. The procedure is versatile and yields optically pure isomers of defined chain length. The synthesis is equally suited for the preparation of lysoPAF analogues and its enantiomers with unsaturation in the long aliphatic chain. First, rac-1(3)-O-alkylglycerol is prepared by alkylation of rac-isopropylideneglycerol with alkyl methanesulfonate followed by acid-catalyzed removal of the ketal group. The primary hydroxy group of alkylglycerol is then protected by tritylation, the secondary hydroxy group is acylated, and the protective trityl group is removed under mild acidic conditions with boric acid on silicic acid, essentially without acyl migration. Condensation of the diradylglycerol with bromoethyl dichlorophosphate in diethyl ether, hydrolysis of the resulting chloride, and nucleophilic displacement of the bromine with trimethylamine gives rac-1-O-alkyl-2-acylglycero-3-phosphocholine in good overall yield. The racemic alkylacylglycerophosphocholine is finally treated with snake venom phospholipase A2 (Ophiophagus hannah) which affords 1-O-alkyl-sn-glycero-3-phosphocholine (lysoPAF) of natural configuration in optically pure form. The "unnatural" 3-O-alkyl-2-O-acyl-sn-glycero-1-phosphocholine enantiomer, which is not susceptible to phospholipase A2 cleavage, gives 3-O-alkyl-sn-glycero-1-phosphocholine upon deacylation with methanolic sodium hydroxide. Homogeneity and structure of the intermediates and final products were ascertained by carbon-13 nuclear magnetic resonance spectroscopy on monomeric solutions.

Lipids in gap junction assembly and function

Gap junctions (GJ) are important regulators of cellular function. They provide channels for the direct movement of small molecules between cells and thus control cell-to-cell transfer of metabolites and the transmission of various stimuli. Gap junctions have been shown to be involved in a multitude of cellular processes ranging from cell synchronization and neuronal function to cell differentiation and carcinogenesis. Much knowledge has been gained in recent years concerning the structure and molecular organization of GJ proteins; yet, the mechanisms that control and modulate gap junction assembly and function are still not well understood. Although it is quite apparent that the GJ proteins assemble in the lipid milieu of the plasma membrane, and that the cluster of proteins assembled in the junction do function in a lipid environment, there is a general paucity of information on the role of lipids in the gap junction assembly process and in the function of gap junctions. The present review is a comprehensive account of current knowledge on gap junction lipids. We also discuss what is known to date on the involvement of lipids in gap junction formation. Special emphasis is being placed on the potential role of membrane cholesterol in gap junction assembly and function.

Lysophosphatidylcholine stabilizes small unilamellar phosphatidylcholine vesicles. Phosphorus-31 NMR evidence for the "wedge" effect

Sonication of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 1-palmitoyl-sn-glycero-3-phosphocholine (lysoPC, up to approximately 30 mol %) produces small unilamellar vesicles (SUV, 250-265 A diameter). Phosphorus-31 NMR of the POPC/lysoPC vesicles gives rise to four distinct peaks for POPC and lysoPC in the outer and in the inner bilayer leaflet which can be used to localize and quantify the phospholipids in both vesicle shells. Addition of paramagnetic ions (3 mM Pr3+) enhances outside/inside chemical shift differences and allows monitoring of membrane integrity by the absence of Pr3+ in the vesicle interior. 31P NMR shows that lysoPC in these highly curved POPC/lysoPC vesicles prefers the outer bilayer leaflet. LysoPC incorporation into POPC SUV furthermore causes a substantial and concentration-dependent decrease in spin-spin relaxations (T*2) of the outside POPC phosphorus signals from 55 ms for pure POPC vesicles (v1/2, 5.8 Hz) to 29.5 ms (v1/2, 10.8 Hz) for POPC/lysoPC vesicles containing 25 mol % lysoPC. Our findings are consistent with the idea of a cone-shaped lysoPC molecule which, for geometric reasons, is preferentially accommodated in the outer bilayer leaflet. LysoPC incorporation into POPC SUV restricts POPC headgroup motion and tightens phospholipid packing, but only in the outer bilayer shell.

Asymmetry of lysophosphatidylcholine/cholesterol vesicles is sensitive to cholesterol modulation

Sonication of lysophosphatidylcholine (lysoPC; 20 mumol/mL) and cholesterol (chol) in aqueous medium produces lamellar structures over a wide range of concentrations. From 25 to 47 mol % cholesterol, electron microscopy (EM) after negative staining showed extended stacklike lamellae about 40 A thick. From 50 to 60 mol % chol, freeze-fracture EM showed homogeneous populations of small unilamellar vesicles averaging 260-310 A in diameter. Phosphorus-31 nuclear magnetic resonance was used to characterize the stacklike lamellae and to measure the distribution of the lysophospholipid between the outer and inner leaflet of the vesicles as a function of sterol concentration. We found that in lysoPC/chol dispersions containing less than equimolar amounts of cholesterol (25-47 mol %), the entire phosphorus signal (40.5 ppm) was shifted downfield by 10.5 ppm upon addition of Pr3+ (2.4 mM), consistent with the stacklike lamellar structures in which all lysoPC head groups are accessible to the ions. By contrast, addition of Pr3+ to lysoPC/chol vesicles containing equimolar or higher amounts of cholesterol (up to 60 mol %) gave rise to two phosphorus peaks. The more intense downfield signal (51.0 ppm) responsive to paramagnetic ions was assigned to lysoPC located in the outer vesicle leaflet. The upfield signal (40.5 ppm), which was not affected by the ions, was assigned to inside lysoPC. For lysoPC/chol (1:1) vesicles, an outside to inside lysophospholipid ratio (Ro/i) of 6.5 was determined. Essentially the same Ro/i value (6.7) was obtained on lysoPC/chol (1:1) vesicles which after dialysis contained only entrapped Pr3+.(ABSTRACT TRUNCATED AT 250 WORDS)

Spectroscopic investigations of the water pool in lecithin reverse micelles

The nature of the water pool formed in the reverse micellar system, lecithin/nonpolar solvent/water, has been investigated by means of near infrared, ultraviolet, fluorescence emission and visible spectroscopic techniques. The three nonpolar solvents chosen in this study were benzene, carbon tetrachloride and cyclohexane. Near infrared spectroscopic studies revealed that the amount of water present in the bulk organic phase is negligible at all water concentrations studied in all three solvents. The results of the polarity probe and 8-anilinonaphthalene sulfonic acid (ANSA) fluorescence emission maxima studies indicate that the polarity of the water pool is much lower than that of bulk water. The difference in polarity between the water pool and bulk water decreases with increasing water concentration in benzene and carbon tetrachloride systems. However, in the cyclohexane system, at a water content of 6 moles of water per mole of lecithin, where the system is known to change from isotropic reverse micelle to anisotropic liquid crystalline state, the polarity of the water pool is found to decrease.

Bilayer asymmetry in lysophosphatidylcholine/cholesterol (1:1) vesicles. A phosphorus-31 NMR study

Prolonged sonication (3 h) of equimolar amounts of lysophosphatidylcholine (lysoPC) and cholesterol (chol) produces small unilamellar vesicles. Phosphorus-31 NMR (32.20 MHz) of the vesicles gave rise to a single peak (40.5 ppm) which was split upon addition of lanthanide ions. An additional, more intense signal appeared downfield near 51.0 ppm due to 2.4 mM Pr3+, upfield near 34.3 ppm due to 5 mM Yb3+. The more intense signals responsive to paramagnetic ions were assigned to lysoPC located in the outer vesicle leaflet; the signal not shifted by the ions was assigned to inside lysoPC. Based on peak intensities, an outside-to-inside lysoPC ratio (Ro/i) of 6.5-6.6 was determined. Essentially the same Ro/i values (6.6-6.8) were obtained when Pr3+ was present only in the vesicle interior or when Pr3+ was on the inside and Pr3+ and Yb3+ were on the outside. Ion leakage did not occur. Our data demonstrate that lysoPC/chol (1:1) vesicles are drastically asymmetric and that lysoPC shows a distinct preference for the outer bilayer leaflet.